File size: 3,357 Bytes
c106aba b5b3a67 c106aba 38368a5 6b8cfb9 ea8b34f 296f52e ea8b34f c106aba 38368a5 6b8cfb9 38368a5 c106aba 06628a1 f2d1246 1e9cc4e f2d1246 1e9cc4e 4f9fa63 346d452 4f9fa63 a1f28e4 6a4c9da 134d885 e273092 134d885 31c8a67 2ce792c 4f9fa63 8b8bd52 6c461f0 ea8b34f 8b8bd52 4f9fa63 c45c6ec 4f9fa63 8b8bd52 3d18c9e c106aba 4f9fa63 ea8b34f c106aba 0b02e4c 0de7587 0b02e4c 38368a5 4f9fa63 90f5dcf 0b02e4c 06628a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
import gradio as gr
import scipy.signal as sps
import sox
def convert(inputfile, outfile):
sox_tfm = sox.Transformer()
sox_tfm.set_output_format(
file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16
)
#print(this is not done)
sox_tfm.build(inputfile, outfile)
def read_file(wav):
sample_rate, signal = wav
signal = signal.mean(-1)
number_of_samples = round(len(signal) * float(16000) / sample_rate)
resampled_signal = sps.resample(signal, number_of_samples)
return resampled_signal
def resampler(input_file_path, output_folder_path):
#output_file_path = output_folder_path + input_file_path.split('/')[-1]
command = (
f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
f"{output_file_path}"
)
subprocess.call(command, shell=True)
def parse_transcription_with_lm(wav_file):
input_values = read_file_and_process(wav_file)
with torch.no_grad():
logits = model(**input_values).logits[0].cpu().numpy()
print(logits)
int_result = processor_with_LM.decode(logits = logits, output_word_offsets=False,
beam_width=128
)
print(int_result)
transcription = int_result.text.replace('<s>','')
return transcription
def read_file_and_process(wav_file):
filename = wav_file.split('.')[0]
convert(wav_file, filename + "16k.wav")
speech, _ = sf.read(filename + "16k.wav")
inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
return inputs
def parse(wav_file, applyLM):
if applyLM:
return parse_transcription_with_lm(wav_file)
else:
return parse_transcription(wav_file)
def parse_transcription(wav_file):
input_values = read_file_and_process(wav_file)
with torch.no_grad():
logits = model(**input_values).logits
#logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
return transcription
model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200"
processor = Wav2Vec2Processor.from_pretrained(model_id)
processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)
input_ = gr.Audio(source="microphone", type="filepath")
#input_ = gr.inputs.Audio(source="microphone", type="numpy")
txtbox = gr.Textbox(
label="Output from model will appear here:",
lines=5
)
chkbox = gr.Checkbox(label="Apply LM", value=False)
gr.Interface(parse, inputs = [input_, chkbox], outputs=txtbox,
streaming=True, interactive=True,
analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False); |